Overview

Dataset statistics

Number of variables24
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory275.8 KiB
Average record size in memory192.1 B

Variable types

Numeric13
Categorical9
Boolean2

Alerts

Age is highly correlated with TotalExperienceYearsHigh correlation
MonthlyIncome is highly correlated with Grade and 1 other fieldsHigh correlation
Grade is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
TotalExperienceYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsInCompany is highly correlated with TotalExperienceYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsInCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsInCompany and 1 other fieldsHigh correlation
YearsWithLineManager is highly correlated with YearsInCompany and 1 other fieldsHigh correlation
Age is highly correlated with Grade and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with Grade and 2 other fieldsHigh correlation
Grade is highly correlated with Age and 3 other fieldsHigh correlation
TotalExperienceYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsInCompany is highly correlated with MonthlyIncome and 5 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsInCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsInCompany and 2 other fieldsHigh correlation
YearsWithLineManager is highly correlated with YearsInCompany and 2 other fieldsHigh correlation
Age is highly correlated with TotalExperienceYearsHigh correlation
MonthlyIncome is highly correlated with Grade and 1 other fieldsHigh correlation
Grade is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
TotalExperienceYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsInCompany is highly correlated with TotalExperienceYears and 2 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsInCompany and 1 other fieldsHigh correlation
YearsWithLineManager is highly correlated with YearsInCompany and 1 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly correlated with DepartmentHigh correlation
Grade is highly correlated with RoleHigh correlation
Role is highly correlated with Department and 1 other fieldsHigh correlation
Age is highly correlated with MonthlyIncome and 3 other fieldsHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with Age and 4 other fieldsHigh correlation
Grade is highly correlated with Age and 6 other fieldsHigh correlation
Role is highly correlated with EducationField and 4 other fieldsHigh correlation
TotalExperienceYears is highly correlated with Age and 7 other fieldsHigh correlation
YearsInCompany is highly correlated with Age and 6 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with Grade and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalExperienceYears and 3 other fieldsHigh correlation
YearsWithLineManager is highly correlated with Grade and 4 other fieldsHigh correlation
EmployeeID has unique values Unique
NumCompaniesWorked has 197 (13.4%) zeros Zeros
YearsInCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithLineManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2022-05-09 07:42:47.110873
Analysis finished2022-05-09 07:43:23.192676
Duration36.08 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

EmployeeID
Real number (ℝ≥0)

UNIQUE

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.865306
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:23.343005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.0243348
Coefficient of variation (CV)0.5874180063
Kurtosis-1.223178906
Mean1024.865306
Median Absolute Deviation (MAD)533.5
Skewness0.01657401958
Sum1506552
Variance362433.2997
MonotonicityStrictly increasing
2022-05-09T07:43:23.591605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
13911
 
0.1%
13891
 
0.1%
13871
 
0.1%
13831
 
0.1%
13821
 
0.1%
13801
 
0.1%
13791
 
0.1%
13771
 
0.1%
13751
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:23.831953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
MonotonicityNot monotonic
2022-05-09T07:43:24.070292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3358
 
3.9%
3858
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Male
882 
Female
588 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male882
60.0%
Female588
40.0%

Length

2022-05-09T07:43:24.300886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:24.442537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male882
60.0%
female588
40.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Married
673 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.902721088
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married673
45.8%
Single470
32.0%
Divorced327
22.2%

Length

2022-05-09T07:43:24.574715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:24.731176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
married673
45.8%
single470
32.0%
divorced327
22.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Education_Level
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2022-05-09T07:43:24.847488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:24.985420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Social Sciences
606 
Health Science
464 
Business
159 
Engineering
132 
Other
82 

Length

Max length15
Median length14
Mean length13.01020408
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSocial Sciences
2nd rowSocial Sciences
3rd rowOther
4th rowSocial Sciences
5th rowHealth Science

Common Values

ValueCountFrequency (%)
Social Sciences606
41.2%
Health Science464
31.6%
Business159
 
10.8%
Engineering132
 
9.0%
Other82
 
5.6%
Human Resources27
 
1.8%

Length

2022-05-09T07:43:25.109579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:25.244437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
social606
23.6%
sciences606
23.6%
health464
18.1%
science464
18.1%
business159
 
6.2%
engineering132
 
5.1%
other82
 
3.2%
human27
 
1.1%
resources27
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
rarely
1043 
frequently
277 
none
150 

Length

Max length10
Median length6
Mean length6.549659864
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrarely
2nd rowfrequently
3rd rowrarely
4th rowfrequently
5th rowrarely

Common Values

ValueCountFrequency (%)
rarely1043
71.0%
frequently277
 
18.8%
none150
 
10.2%

Length

2022-05-09T07:43:25.390050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:25.526442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
rarely1043
71.0%
frequently277
 
18.8%
none150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.54217687
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development961
65.4%
Sales446
30.3%
Human Resources63
 
4.3%

Length

2022-05-09T07:43:25.647016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:25.778921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
research961
27.8%
961
27.8%
development961
27.8%
sales446
12.9%
human63
 
1.8%
resources63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Commute
Real number (ℝ≥0)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:25.905626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
MonotonicityNot monotonic
2022-05-09T07:43:26.127482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:26.366611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
MonotonicityNot monotonic
2022-05-09T07:43:26.628519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
27413
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
55623
 
0.2%
34523
 
0.2%
23803
 
0.2%
63473
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:26.874784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786044
Coefficient of variation (CV)0.4972915967
Kurtosis-1.2149561
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01857780789
Sum21040262
Variance50662878.17
MonotonicityNot monotonic
2022-05-09T07:43:27.356353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42233
 
0.2%
91503
 
0.2%
95582
 
0.1%
128582
 
0.1%
220742
 
0.1%
253262
 
0.1%
90962
 
0.1%
130082
 
0.1%
123552
 
0.1%
77442
 
0.1%
Other values (1417)1448
98.5%
ValueCountFrequency (%)
20941
0.1%
20971
0.1%
21041
0.1%
21121
0.1%
21221
0.1%
21252
0.1%
21371
0.1%
22271
0.1%
22431
0.1%
22531
0.1%
ValueCountFrequency (%)
269991
0.1%
269971
0.1%
269681
0.1%
269591
0.1%
269561
0.1%
269331
0.1%
269141
0.1%
268971
0.1%
268941
0.1%
268621
0.1%

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:27.544158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5090999
Coefficient of variation (CV)0.5028240288
Kurtosis-1.203822808
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003518568352
Sum1179654
Variance162819.5937
MonotonicityNot monotonic
2022-05-09T07:43:27.727676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6916
 
0.4%
4085
 
0.3%
5305
 
0.3%
13295
 
0.3%
10825
 
0.3%
3295
 
0.3%
8294
 
0.3%
14694
 
0.3%
2674
 
0.3%
2174
 
0.3%
Other values (876)1423
96.8%
ValueCountFrequency (%)
1021
 
0.1%
1031
 
0.1%
1041
 
0.1%
1051
 
0.1%
1061
 
0.1%
1071
 
0.1%
1091
 
0.1%
1113
0.2%
1151
 
0.1%
1162
0.1%
ValueCountFrequency (%)
14991
 
0.1%
14981
 
0.1%
14962
0.1%
14953
0.2%
14921
 
0.1%
14904
0.3%
14881
 
0.1%
14853
0.2%
14821
 
0.1%
14802
0.1%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:27.955374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.32942759
Coefficient of variation (CV)0.3085304415
Kurtosis-1.196398456
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.0323109529
Sum96860
Variance413.2856263
MonotonicityNot monotonic
2022-05-09T07:43:28.211025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6629
 
2.0%
9828
 
1.9%
4228
 
1.9%
4828
 
1.9%
8428
 
1.9%
5727
 
1.8%
7927
 
1.8%
9627
 
1.8%
5426
 
1.8%
5226
 
1.8%
Other values (61)1196
81.4%
ValueCountFrequency (%)
3019
1.3%
3115
1.0%
3224
1.6%
3319
1.3%
3412
0.8%
3518
1.2%
3618
1.2%
3718
1.2%
3813
0.9%
3917
1.2%
ValueCountFrequency (%)
10019
1.3%
9920
1.4%
9828
1.9%
9721
1.4%
9627
1.8%
9523
1.6%
9422
1.5%
9316
1.1%
9225
1.7%
9118
1.2%

Grade
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Length

2022-05-09T07:43:28.438030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:28.574287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Role
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length18
Mean length18.0707483
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive326
22.2%
Research Scientist292
19.9%
Laboratory Technician259
17.6%
Manufacturing Director145
9.9%
Healthcare Representative131
8.9%
Manager102
 
6.9%
Sales Representative83
 
5.6%
Research Director80
 
5.4%
Human Resources52
 
3.5%

Length

2022-05-09T07:43:28.719483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:28.875464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sales409
14.4%
research372
13.1%
executive326
11.5%
scientist292
10.3%
laboratory259
9.1%
technician259
9.1%
director225
7.9%
representative214
7.5%
manufacturing145
 
5.1%
healthcare131
 
4.6%
Other values (3)206
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Satisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2022-05-09T07:43:29.047174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-09T07:43:29.181423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:29.284416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
MonotonicityNot monotonic
2022-05-09T07:43:29.458491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False1054
71.7%
True416
 
28.3%
2022-05-09T07:43:29.588590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

TotalExperienceYears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:29.706176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
MonotonicityNot monotonic
2022-05-09T07:43:29.953374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

YearsInCompany
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:30.187773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
MonotonicityNot monotonic
2022-05-09T07:43:30.417427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:30.627213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137035
Coefficient of variation (CV)0.856685128
Kurtosis0.4774207735
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9173631563
Sum6217
Variance13.12712197
MonotonicityNot monotonic
2022-05-09T07:43:30.833725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:31.034087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
MonotonicityNot monotonic
2022-05-09T07:43:31.237063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithLineManager
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2022-05-09T07:43:31.434329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
MonotonicityNot monotonic
2022-05-09T07:43:31.640917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1233 
True
237 
ValueCountFrequency (%)
False1233
83.9%
True237
 
16.1%
2022-05-09T07:43:31.788783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2022-05-09T07:43:19.219739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:51.058314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:53.123854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:55.838543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:58.412706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:01.319531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:03.790099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.830208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.997695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.756987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:11.758725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:14.214633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:16.676384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:19.410449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:51.240441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:53.309677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:56.031899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:58.918849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:01.498771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:03.975553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.967121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.124289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.903590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:11.905323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:14.355439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:16.860893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:19.600736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:51.420700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:53.488122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:56.221234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:59.109173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:01.675768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:04.159543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:06.107304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.250424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.045381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:12.050901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:14.494012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:17.043617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:19.800172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:51.623831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:53.679906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:56.417489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:59.304447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:01.864288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:04.348290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:06.533890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.386307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.188776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:12.204215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:14.686191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:17.242769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:20.293050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:51.772525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:53.872733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:56.623617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:59.507107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:02.062110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:04.544744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:06.683699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.526158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.341556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:12.639311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:14.887372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:17.442843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:20.485326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:51.902234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:54.056636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:56.813458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:59.699446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:02.248940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:04.679536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:06.816210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.655186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.482525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:12.785643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:15.077672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:17.629823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:20.684179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.043232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:54.244648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:57.011787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:59.899289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:02.438681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:04.819488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:06.964574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.789048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.647292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:12.940435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:15.272561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:17.824347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:20.889205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.188497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:54.441684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:57.214540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:00.104215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:02.646220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:04.965761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.111883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:08.931670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.804118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:13.105436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:15.472698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:18.023904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:21.084067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.321397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:54.632879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:57.403219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:00.296438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:02.827818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.097498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.246567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.054744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:10.949075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:13.251774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:15.660584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:18.208860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:21.295283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.469631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:54.857506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:57.607491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:00.502374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:03.026500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.244562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.399559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.196482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:11.110411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:13.411496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:15.867534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:18.412782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:21.462815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.615241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:55.100026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:57.810328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:00.708977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:03.218758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.392291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.550632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.334976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:11.273390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:13.642404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:16.073625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:18.614867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:21.625465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.758163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:55.393797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:58.010069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:00.918232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:03.408599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.539689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.698772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.474465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:11.435318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:13.851040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:16.273468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:18.817496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:21.830084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:52.919102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:55.629279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:42:58.209996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:01.121642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:03.596184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:05.683327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:07.849793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:09.613615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:11.593866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:14.055168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:16.476528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-09T07:43:19.019353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-09T07:43:31.880010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T07:43:32.188444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T07:43:32.488700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T07:43:32.783231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T07:43:33.316832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T07:43:22.225387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T07:43:22.943672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

EmployeeIDAgeGenderMaritalStatusEducation_LevelEducationFieldBusinessTravelDepartmentCommuteMonthlyIncomeMonthlyRateDailyRateHourlyRateGradeRoleSatisfactionNumCompaniesWorkedOverTimeTotalExperienceYearsYearsInCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithLineManagerAttrition
0141FemaleSingle2Social SciencesrarelySales15993194791102942Sales Executive48Yes86405Yes
1249MaleMarried1Social SciencesfrequentlyResearch & Development8513024907279612Research Scientist21No1010717No
2437MaleSingle2OtherrarelyResearch & Development2209023961373921Laboratory Technician36Yes70000Yes
3533FemaleMarried4Social SciencesfrequentlyResearch & Development32909231591392561Research Scientist31Yes88730No
4727MaleMarried1Health SciencerarelyResearch & Development2346816632591401Laboratory Technician29No62222No
5832MaleSingle2Social SciencesfrequentlyResearch & Development23068118641005791Laboratory Technician40No87736No
61059FemaleMarried3Health SciencerarelyResearch & Development3267099641324811Laboratory Technician14Yes121000No
71130MaleDivorced1Social SciencesrarelyResearch & Development242693133351358671Laboratory Technician31No11000No
81238MaleSingle3Social SciencesfrequentlyResearch & Development2395268787216443Manufacturing Director30No109718No
91336MaleMarried3Health SciencerarelyResearch & Development275237165771299942Healthcare Representative36No177777No

Last rows

EmployeeIDAgeGenderMaritalStatusEducation_LevelEducationFieldBusinessTravelDepartmentCommuteMonthlyIncomeMonthlyRateDailyRateHourlyRateGradeRoleSatisfactionNumCompaniesWorkedOverTimeTotalExperienceYearsYearsInCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithLineManagerAttrition
1460205429FemaleSingle4Health SciencerarelyResearch & Development2837858489468731Research Scientist11No55404No
1461205550MaleDivorced3BusinessrarelySales281085416586410393Sales Executive14Yes203220Yes
1462205639FemaleMarried1BusinessrarelySales24120318828722604Sales Executive40No2120996No
1463205731MaleSingle3Health SciencenoneResearch & Development599363787325742Manufacturing Director10No109417No
1464206026FemaleSingle3OtherrarelySales52966213781167301Sales Representative30No54200No
1465206136MaleMarried2Health SciencefrequentlyResearch & Development23257112290884412Laboratory Technician44No175203No
1466206239MaleMarried1Health SciencerarelyResearch & Development6999121457613423Healthcare Representative14No97717No
1467206427MaleMarried3Social SciencesrarelyResearch & Development461425174155872Manufacturing Director21Yes66203No
1468206549MaleMarried3Health SciencefrequentlySales25390132431023632Sales Executive22No179608No
1469206834MaleMarried3Health SciencerarelyResearch & Development8440410228628822Laboratory Technician32No64312No